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1417. On Reducing Medically Unnecessary Procedures Through Analytics: The Design of Financial Incentives for Maternity Care
Invited abstract in session MC-15: Healthcare Analytics, stream OR in Health Services (ORAHS).
Monday, 12:30-14:00Room: 18 (building: 116)
Authors (first author is the speaker)
| 1. | Beste Kucukazici
|
| Smith Business School, Queen's University | |
| 2. | Emily Zhu
|
| Information Systems & Analytics, Texas State University | |
| 3. | Ting Wu
|
| Department of Mathematics, Nanjing University |
Abstract
This work focuses on the design of financial incentives to reduce medically unnecessary C-sections, resulting in enhanced birth quality with alleviated economic burden for healthcare payers. To this end, we develop an innovative semi-supervised fuzzy clustering algorithm to classify pregnant women into low- and high-risk groups by analyzing approximately 18 million birth records from US. Our experiments on real-life and synthetic data demonstrate the efficiency of our algorithm for large datasets. Then, we validate the optimal delivery methods for two risk groups through post-delivery outcomes for the mother and the newborn by using inferential statistical analysis. Furthermore, we develop a metric to quantify the maternity risk index to be used in stylized analytical models. Next, we develop an analytical framework based on a principle-agent model to analyze the effect of different payment schemes from the quality of care and cost perspectives. We propose three payment systems, hybrid payment, risk-sharing model, and penalty contracts to alleviate the shortcomings of fee-for-service and bundled payment schemes, thereby facilitating system optimal decisions. Finally, we present a simulation-based numerical study to empirically verify our analytical results.
Keywords
- Health Care
- Analytics and Data Science
Status: accepted
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